Using prediction markets of market scoring rule to forecast infectious diseases: a case study in Taiwan

Abstract

BackgroundThe Taiwan CDC relied on the historical average number of disease cases or rate (AVG) to depict the trend of epidemic diseases in Taiwan. By comparing the historical average data with prediction markets, we show that the latter have a better prediction capability than the former. Given the volatility of the infectious diseases in Taiwan, historical average is unlikely to be an effective prediction mechanism.MethodsWe designed and built the Epidemic Prediction Markets (EPM) system based upon the trading mechanism of market scoring rule. By using this system, we aggregated dispersed information from various medical professionals to predict influenza, enterovirus, and dengue fever in Taiwan.ResultsEPM was more accurate in 701 out of 1,085 prediction events than the traditional baseline of historical average and the winning ratio of EPM versus AVG was 64.6 % for the target week. For the absolute prediction error of five diseases indicators of three infectious diseases, EPM was more accurate for the target week than AVG except for dengue fever confirmed cases. The winning ratios of EPM versus AVG for the confirmed cases of severe complicated influenza case, the rate of enterovirus infection, and the rate of influenza-like illness in the target week were 69.6 %, 83.9 and 76.0 %, respectively; instead, for the prediction of the confirmed cases of dengue fever and the confirmed cases of severe complicated enterovirus infection, the winning ratios of EPM were all below 50 %.ConclusionsExcept confirmed cases of dengue fever, EPM provided accurate, continuous and real-time predictions of four indicators of three infectious diseases for the target week in Taiwan and outperformed the historical average data of infectious diseases.

Overpricing persistence in experimental asset markets with intrinsic uncertainty

“…The primary purpose of this strain of research is the investigation and modelling of opinion aggregation, measurement of prediction accuracy for political and sports events and eliciting group decisions over the market coordination. They have been used to successfully predict political elections (Forsythe et al, 1992;Berg et al, 2008;Forsythe et al, 1999;Hansen et al, 2004), outcry of infectious diseases (Polgreen et al, 2007;Tung et al, 2015), sports outcomes (Kain and Logan, 2014) and new product blockbusters (Cowgill et al, 2009;Ho and Chen, 2007;Elberse and Eliashberg, 2003).…”

Chimeric forecasting: combining probabilistic predictions from computational models and human judgment

“…Prediction markets have been developed to predict infectious diseases such as the 2009 swine flu, seasonal influenza, enterovirus, and dengue fever [54][55][56]. A prediction market provides participants an initial amount of "money" to spend on future events and allows participants to place higher bids on events they think are more likely to occur.…”

Using prediction polling to harness collective intelligence for disease forecasting

“…Prediction markets, in which forecasters can buy and sell contracts related to outcomes, were first used for educational purposes at the University of Iowa beginning in 1988 [ 4 – 7 ]. Originally used to forecast political outcomes, currency prices, movie box office returns, and book sales, electronic markets have also been tested in forecasting health-related events [ 8 , 9 ]. These markets aimed to aggregate diverse opinions and expertise in public health, medicine, and related fields, which would provide information that could improve response to infectious disease emergencies.…”